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2025 | OriginalPaper | Chapter

A Review on Ensemble Techniques and Its Application on Social Bot Detection

Authors : Jwala Sharma, Samarjeet Borah

Published in: Advances in Communication, Devices and Networking

Publisher: Springer Nature Singapore

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Abstract

Social media attracts all kinds of activities, including product marketing, celebrity marketing, and also it serves as platform for promoting political agenda. As it is gaining popularity from all various source, it has also attracted spammers and automated accounts that are responsible for spreading the misinformation and influencing the audience. In this context, there is a need to properly classify the social media account as bot account or human account. For classification and detection of social bots, different machine learning, deep learning techniques are implemented. In this paper, we have focused on ensemble technique for classification of social bot. Considering heterogeneous base classifier, such as decision tree, logistic regression and k-neighbor classifier, an ensemble model has been built, that combines the prediction of base classifier, and gives the final prediction. The ensemble approach that has been implemented are, majority voting, random forest and bagging with decision tree.

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previous chapter An Experimental Analysis of Machine Learning Models for Diabetes Classification
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Metadata
Title
A Review on Ensemble Techniques and Its Application on Social Bot Detection
Authors
Jwala Sharma
Samarjeet Borah
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Advances in Communication, Devices and Networking

Print ISBN: 978-981-9764-64-8

Electronic ISBN: 978-981-9764-65-5

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-97-6465-5_12